↓ Skip to main content

SAMSA2: a standalone metatranscriptome analysis pipeline

Overview of attention for article published in BMC Bioinformatics, May 2018
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

twitter
70 X users

Citations

dimensions_citation
108 Dimensions

Readers on

mendeley
347 Mendeley
citeulike
1 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
SAMSA2: a standalone metatranscriptome analysis pipeline
Published in
BMC Bioinformatics, May 2018
DOI 10.1186/s12859-018-2189-z
Pubmed ID
Authors

Samuel T. Westreich, Michelle L. Treiber, David A. Mills, Ian Korf, Danielle G. Lemay

Abstract

Complex microbial communities are an area of growing interest in biology. Metatranscriptomics allows researchers to quantify microbial gene expression in an environmental sample via high-throughput sequencing. Metatranscriptomic experiments are computationally intensive because the experiments generate a large volume of sequence data and each sequence must be compared with reference sequences from thousands of organisms. SAMSA2 is an upgrade to the original Simple Annotation of Metatranscriptomes by Sequence Analysis (SAMSA) pipeline that has been redesigned for standalone use on a supercomputing cluster. SAMSA2 is faster due to the use of the DIAMOND aligner, and more flexible and reproducible because it uses local databases. SAMSA2 is available with detailed documentation, and example input and output files along with examples of master scripts for full pipeline execution. SAMSA2 is a rapid and efficient metatranscriptome pipeline for analyzing large RNA-seq datasets in a supercomputing cluster environment. SAMSA2 provides simplified output that can be examined directly or used for further analyses, and its reference databases may be upgraded, altered or customized to fit the needs of any experiment.

X Demographics

X Demographics

The data shown below were collected from the profiles of 70 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 347 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 347 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 71 20%
Researcher 64 18%
Student > Master 45 13%
Student > Doctoral Student 24 7%
Student > Bachelor 22 6%
Other 42 12%
Unknown 79 23%
Readers by discipline Count As %
Agricultural and Biological Sciences 89 26%
Biochemistry, Genetics and Molecular Biology 66 19%
Environmental Science 29 8%
Immunology and Microbiology 28 8%
Computer Science 12 3%
Other 33 10%
Unknown 90 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 34. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 March 2023.
All research outputs
#1,176,182
of 25,523,622 outputs
Outputs from BMC Bioinformatics
#114
of 7,713 outputs
Outputs of similar age
#25,360
of 344,417 outputs
Outputs of similar age from BMC Bioinformatics
#3
of 111 outputs
Altmetric has tracked 25,523,622 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,713 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done particularly well, scoring higher than 98% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 344,417 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 111 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.